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Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer
PURPOSE: Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of SLNs by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korean Cancer Association
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7577824/ https://www.ncbi.nlm.nih.gov/pubmed/32599974 http://dx.doi.org/10.4143/crt.2020.337 |
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author | Kim, Young-Gon Song, In Hye Lee, Hyunna Kim, Sungchul Yang, Dong Hyun Kim, Namkug Shin, Dongho Yoo, Yeonsoo Lee, Kyowoon Kim, Dahye Jung, Hwejin Cho, Hyunbin Lee, Hyungyu Kim, Taeu Choi, Jong Hyun Seo, Changwon Han, Seong il Lee, Young Je Lee, Young Seo Yoo, Hyung-Ryun Lee, Yongju Park, Jeong Hwan Oh, Sohee Gong, Gyungyub |
author_facet | Kim, Young-Gon Song, In Hye Lee, Hyunna Kim, Sungchul Yang, Dong Hyun Kim, Namkug Shin, Dongho Yoo, Yeonsoo Lee, Kyowoon Kim, Dahye Jung, Hwejin Cho, Hyunbin Lee, Hyungyu Kim, Taeu Choi, Jong Hyun Seo, Changwon Han, Seong il Lee, Young Je Lee, Young Seo Yoo, Hyung-Ryun Lee, Yongju Park, Jeong Hwan Oh, Sohee Gong, Gyungyub |
author_sort | Kim, Young-Gon |
collection | PubMed |
description | PURPOSE: Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of SLNs by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin–stained frozen tissue sections of SLNs in breast cancer patients. MATERIALS AND METHODS: A total of 297 digital slides were obtained from frozen SLN sections, which include post–neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for 6 weeks with two P40 GPUs. The algorithms were assessed in terms of the area under receiver operating characteristic curve (AUC). RESULTS: The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. CONCLUSION: In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative SLN biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting. |
format | Online Article Text |
id | pubmed-7577824 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Korean Cancer Association |
record_format | MEDLINE/PubMed |
spelling | pubmed-75778242020-10-26 Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer Kim, Young-Gon Song, In Hye Lee, Hyunna Kim, Sungchul Yang, Dong Hyun Kim, Namkug Shin, Dongho Yoo, Yeonsoo Lee, Kyowoon Kim, Dahye Jung, Hwejin Cho, Hyunbin Lee, Hyungyu Kim, Taeu Choi, Jong Hyun Seo, Changwon Han, Seong il Lee, Young Je Lee, Young Seo Yoo, Hyung-Ryun Lee, Yongju Park, Jeong Hwan Oh, Sohee Gong, Gyungyub Cancer Res Treat Original Article PURPOSE: Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of SLNs by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin–stained frozen tissue sections of SLNs in breast cancer patients. MATERIALS AND METHODS: A total of 297 digital slides were obtained from frozen SLN sections, which include post–neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for 6 weeks with two P40 GPUs. The algorithms were assessed in terms of the area under receiver operating characteristic curve (AUC). RESULTS: The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. CONCLUSION: In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative SLN biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting. Korean Cancer Association 2020-10 2020-06-30 /pmc/articles/PMC7577824/ /pubmed/32599974 http://dx.doi.org/10.4143/crt.2020.337 Text en Copyright © 2020 by the Korean Cancer Association This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Kim, Young-Gon Song, In Hye Lee, Hyunna Kim, Sungchul Yang, Dong Hyun Kim, Namkug Shin, Dongho Yoo, Yeonsoo Lee, Kyowoon Kim, Dahye Jung, Hwejin Cho, Hyunbin Lee, Hyungyu Kim, Taeu Choi, Jong Hyun Seo, Changwon Han, Seong il Lee, Young Je Lee, Young Seo Yoo, Hyung-Ryun Lee, Yongju Park, Jeong Hwan Oh, Sohee Gong, Gyungyub Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer |
title | Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer |
title_full | Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer |
title_fullStr | Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer |
title_full_unstemmed | Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer |
title_short | Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer |
title_sort | challenge for diagnostic assessment of deep learning algorithm for metastases classification in sentinel lymph nodes on frozen tissue section digital slides in women with breast cancer |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7577824/ https://www.ncbi.nlm.nih.gov/pubmed/32599974 http://dx.doi.org/10.4143/crt.2020.337 |
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